Summary
The project aims to optimize and evaluate an AI-assisted mobile high-resolution microendoscope (AI-mHRME) for the early detection and management of esophageal squamous cell neoplasia (ESCN) in diverse populations in the USA and Brazil, focusing on clinical impact and implementation potential.
What they want
The project will build on existing global data to optimize an AI-mHRME and evaluate its clinical impact and implementation potential in ethnically and socioeconomically diverse populations in the USA and Brazil. A stakeholder-engaged approach will be used to evaluate barriers, acceptability, appropriateness, and feasibility of using AI-mHRME in ESCN management and to determine contextual factors influencing adoption. The data obtained will facilitate implementation and dissemination of innovative, AI-assisted cancer screening strategies in diverse populations and other cancers.
Deliverables
- Optimized AI-mHRME
- Evaluation of clinical impact of AI-mHRME in diverse populations
- Evaluation of implementation potential of AI-mHRME in diverse populations
- Evaluation of barriers, acceptability, appropriateness, and feasibility of AI-mHRME
- Identification of contextual factors influencing AI-mHRME adoption
- Data to facilitate implementation and dissemination of AI-assisted cancer screening strategies
Technical requirements
- Mobile, high-resolution microendoscope (mHRME) technology
- Deep-learning software algorithms for automated detection of neoplastic images
- Artificial intelligence (AI) integration for quantitative interpretation
